Socioeconomic disparities and green space associated with myopia among Chinese school-aged students: A population-based cohort study

Background There is increasing evidence on the link between environmental factors and myopia in children and adolescents, yet with inconsistent conclusions. We investigated the associations between socioeconomic inequalities and green space with myopia in school-aged students participating in the Tianjin Child and Adolescent Research of Eye (TCARE) study. Methods We obtained data from a population-based dynamic cohort study conducted in Tianjin, China, in 2021 and followed up in 2022. We included 1 245 271 participants from 16 districts with an average age of 11.6 years (standard deviation = 3.3) in our analysis. We synthesized their area-level SES through a prediction model that combined economic, educational, and health care variables and assessed the greenness levels surrounding the school using the Normalized Difference Vegetation Index (NDVI) based on data obtained through satellite remote sensing. We performed generalised linear mixed effects analyses for each myopia outcome separately, with adjustments for students’ sex, years of education completed, and the school’s geographical location. Results We observed that students living in low SES areas had the highest prevalence of myopia (60.7%) in the last screening in 2022, as well as a higher incidence of one-year myopia (26.4%) compared to those residing in middle SES areas (22.7%). With a 0.1 increase in the 250, 500, and 1000 m buffer NDVI, the prevalence of myopia dropped by 6.3% (odds ratio (OR) = 0.937; 95% confidence interval (CI) = 0.915, 0.960), 7.7% (OR = 0.923; 95% CI = 0.900, 0.946), and 8.7% (OR = 0.913; 95% CI = 0.889, 0.937), respectively. The interaction analysis showed that low SES and low greenness exacerbate the prevalence of myopia. Findings from longitudinal analyses consistently demonstrated a correlation between higher values of NDVI and a slower progression of myopia. These findings remained robust across sensitivity analyses, including for variables on parental myopia and students’ behaviors. Conclusions Exposure to green spaces could play a crucial role in slowing the progression of myopia among school-aged students. Myopia control policies should prioritise young populations residing in low SES areas with limited access to green spaces, as they face the highest potential risks.


Supplementary appendix
S1. Description of the questions regarding parental myopia and children's behaviors used in this study.
(Page 2) Table S1.1:Description of questions used in this study derived from the TCARE behaviors questionnaire.
(Page 3) S2.Instruction to derive the area-level socioeconomic status.
(Page 5) Table S2.1:Description of variables used for create the area-level socio-economic status indicator.
(Page 6) Table S2.2:Variables in the prediction model of socio-economic status by principal component analysis.
( (Page 9) Table S3.1.1:Characteristics of myopia and non-myopia among 645,022 boys in the Tianjin Child and Adolescent Research of Eye study.
(Page 10) Table S3.1.2:Characteristics of myopia and non-myopia among 600,249 girls in the Tianjin Child and Adolescent Research of Eye study.
(Page 11) Table S3.2.1: Characteristics of new myopia and non-myopic among 446,641 students during one year follow-up in the Tianjin Child and Adolescent Research of Eye study.

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Table S3.Table S3.3:The estimated regression coefficients of changes in uncorrected visual acuity for both eyes among 990,578 students according to green space exposure in different buffers.
(Page 15) Table S3.4:The estimated regression coefficients of changes in sphere equivalent for both eyes among 990,578 students according to green space exposure in different buffers.
(Page 16) S4.Supporting tables for the results of sensitivity analysis in this study.
(Page 17) Table S4.1:Results of the sensitivity analysis of factors associated with myopia among 29, 971 students in the TCARE study.
(Page 18) Table S4.2:Odds ratios (ORs) of new myopia among 10,652 students associated with socioeconomic statuses and green space exposure.

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Table S4.3:The estimated regression coefficients of changes in uncorrected visual acuity for both eyes among 21,251 students according to green space exposure in different buffers.
(Page 20) Table S4.4:The estimated regression coefficients of changes in sphere equivalent for both eyes among 21,251 students according to green space exposure in different buffers.
(Page 21) The area-level SES in this study was derived by a prediction model combining economic, educational, and healthcare variables to estimate the level of development of a district.The data were derived from the Tianjin Statistical Bureau's 2021 annual data, as published in the "2022 Tianjin Statistical Yearbook".From this yearbook, we gathered regional data including population, the gross domestic product (GDP) of each district (in hundreds of millions of yuan), educational budgetary expenditures (in hundreds of millions of yuan), health and wellness expenditures (in hundreds of millions of yuan), the number of licensed (assistant) doctors per 1,000 people, the number of registered nurses per 1,000 people, and the number of beds in healthcare institutions per 1,000 people.
Using this data, four additional variables were generated: 'regional GDP per capita', 'percentage of educational budgetary expenditures in GDP', 'percentage of health and wellness expenditures in GDP', and 'number of doctors and nurses per 1,000 persons'.These variables, along with the 'number of beds in healthcare institutions per 1,000 persons', were utilized to create the area-level SES indicator for this study.Table S2.1 presents the data derived from the '2022 Tianjin Statistical Yearbook(https://stats.tj.gov.cn/nianjian/2022nj/zk/indexch.htm)'.
These data were standardized and subjected to principal component analysis.The Z-values of each indicators for each district after standardization are shown in Table S2.2,Five standardized variables were entered into the principal component analysis: 'regional GDP per capita', 'number of beds in healthcare institutions per 1,000 persons', 'number of doctors and nurses per 1,000 persons', 'percentage of educational budgetary expenditures in GDP', and 'percentage of health and wellness expenditures in GDP' The following formula was used: The correlation matrix for each indicator was calculated using standardized data, followed by eigenvalue decomposition.The first two principal components were then selected based on the magnitudes of the eigenvalues to explain the majority of the variance.After the dimensionality reduction analysis, the data were combined into a composite indicator , and the evaluation function was constructed as follows: The Table S2.2 shows a summary of the results of the principal component analysis.
Table S3.4:The estimated regression coefficients of changes in sphere equivalent for both eyes among 990,578 students according to green space exposure in different buffers.

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Table S3    Table S3.4:The estimated regression coefficients of changes in sphere equivalent for both eyes among 990,578 students according to green space exposure in different buffers.

S4. Supporting tables for the results of sensitivity analysis in this study.
In the sensitivity analysis (n = 29,971), 51.9% of these students were males (n=15,569) and 48.1% were females (n = 14,402).The prevalence of myopia in this subgroup was 53.2%.There was no statistically significant difference in the distribution of sex between the subgroup and the overall group (P > 0.05).The students in this subgroup were younger, with an average age of 10.4 ± 2.8 years, compared to the overall group (P < 0.05).This suggests that parents of younger students were more likely to complete the questionnaire.In addition, student volunteers from low SES areas showed unwillingness to complete the questionnaire.As a result, there was a smaller proportion of students from low SES areas (n = 2,215), accounting for 7.4% of the total in this subgroup.
For the correlation between SES, green space, and the prevalence of myopia, these findings remained robust across sensitivity analyses.Students living in low SES areas exhibited a higher prevalence of myopia compared to those residing in middle SES districts, with ORs ranging from 1.312 to 1.335.The ORs and 95% CIs for NDVI at the 250-meter, 500-meter, and 1000-meter buffer were 0.949 (95% CI=0.913,0.987),0.924 (95% CI=0.887,0.964),and 0.948 (95% CI=0.906,0.991),respectively.Moreover, students who frequented green spaces more often demonstrated a lower prevalence of myopia, with ORs ranging from 0.921 to 0.922.These findings are summarized in Table S4.1.
During the follow-up period, a total of 21,251 participants were included.Among them, 10,652 students were nonmyopic at the initial screening, and 2,881 (27%) were newly diagnosed with myopia.The sensitivity analysis confirmed the robustness of the findings regarding SES.However, no evidence suggested a relationship between green space and the incidence of new myopia in the subgroup (Table S4.2).Nevertheless, higher greenness was correlated with a slower decline in UCVA (NDVI250m, NDVI500m, and NDVI1000m) and SER (only for NDVI250m).
Students living in low SES areas exhibited a more rapid decline in UCVA.In contrast, those residing in high SES areas demonstrated a slower decline in UCVA than other SES groups (Table S4.3).Students living in high SES areas also experienced a rapid decline in SER (Table S4.4).
This study consistently found an association between parental myopia and poorer myopia outcomes in all sensitivity analyses.On a positive note, maintaining a reading distance of less than one foot from a book benefitted myopia outcomes.However, spending more time using screens was correlated with a faster decline in SER (Table S4.4).Moreover, students who frequently used desk lamps, such as LED, incandescent, or fluorescent lamps, while doing homework had a higher prevalence of myopia and experienced a rapid decline in UCVA compared to those who rarely used desk lamps.Years of education completed, P50 (P25, P75) 4.9 (2.9, 6.9) 2.9 (0.9, 4.9) Bold: P < 0.05.Logistic regression models were used to determine the relationships between potential correlates and the prevalence of myopia.In the final step of regression, geographic area, outdoor play, screen time, sleeping with night lights, salt intake, snack and dessert intake, seafood intake, and soft drinks intake were removed from the model.Bold: P < 0.05.Logistic regression models were used to determine the relationships between potential correlates and the prevalence of myopia.In the final step of regression, normalized difference vegetation index, geographic area, outdoor play, frequency of going to green spaces, screen time, type of desk lamp, salt intake, snack and dessert intake, seafood intake, and soft drinks intake were removed from the model.Bold: P < 0.05.UCVA: uncorrected distance visual acuity.Logistic regression models were used to determine the relationships between potential correlates and the prevalence of myopia.In the final step of regression, geographic area, outdoor play, frequency of going to green spaces, screen time, sleeping with night lights, salt intake, snack and dessert intake, seafood intake, and soft drinks intake were removed from the model.

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Table S4.4:The estimated regression coefficients of changes in sphere equivalent for both eyes among 21,251 students according to green space exposure in different buffers.correlates and the prevalence of myopia.In the final step of regression, geographic area, outdoor play, frequency of going to green spaces, reading distance, type of desk lamp, sleeping with night lights, salt intake, snack and dessert intake, seafood intake, and soft drinks intake were removed from the model.

Factors
In model 2 and model 3, NDVI was also removed from the models.
7) Figure S2.1.Colour-coded diagram of the socio-economic status of Tianjin districts.(Page 8) S3.Supporting tables and figure for the supplementary results in this study.
2.2: Characteristics of new myopia and non-myopic in right eyes among 503,225 students during one year follow-up in the Tianjin Child and Adolescent Research of Eye study.(Page 13)Table S3.2.3: Characteristics of new myopia and non-myopic in left eyes among 522,090 students during one year follow-up in the Tianjin Child and Adolescent Research of Eye study.(Page 14)
< 0.05.SER: spherical equivalent refraction.Logistic regression models were used to determine the relationships between potential

Table S2 .2: Variables in the prediction model of socio-economic status by principal component analysis. District Z- score of X1 Z- score of X2 Z- score of X3 Z- score of X4 Z- score of X5 Coefficients 1 Coefficients 2 f
health and wellness expenditures in GDP.

Table S3 .3: The estimated regression coefficients of changes in uncorrected visual acuity for both eyes among 990,578 students according to green space exposure in different buffers.
Bold: P < 0.05.UCVA: uncorrected distance visual acuity.